Overview

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Data Scientist (m/f/d) Remote Option

Location:
Nürnberg, DE, 90411

Job Requisition ID: 6303

Schaeffler is a dynamic global technology company and its success has been a result of its entrepreneurial spirit and long history of private ownership. Does that sound interesting to you? As a partner to all of the major automobile manufacturers, as well as key players in the aerospace and industrial sectors, we offer you many development opportunities.
General Information

We – the Data Science Solutions Unit at SCHAEFFLER are looking for you! We create user-centric digital solutions that we also use internationally. In doing so, we use one of the most important assets of our company: data!
We – that are several agile teams consisting of colleagues from all over the world with the highest expertise in the areas of data science, artificial intelligence & machine learning, holistic user experience, cloud computing & software engineering, integration & architecture.
Award-winning applications speak for our success.

We are also happy to receive applications from interested Young Professionals (m/f/d).

Your Key Responsibilities

Create, deploy and operate Data Science Solutions in the context of machine lifetime analysis as part of a cross-functional, agile product team
Collaborate closely with business stakeholders and condition monitoring specialists
Carry out time series data analyses for the prediction of machine states
Research scientific papers and apply relevant techniques to industry problems along with thorough validation of results
Become a fully-committed member of a product team which takes ownership of new AI services from literature research to model serving
Become an active part of our strong Data Science Solutions department where exchange and networking beyond team borders is a welcome source of inspiration

Your Qualifications

Completed degree in (business) informatics, mathematics or comparable qualification
Ideally some years of professional experience in the field of statistics, vibration analysis and time series analysis, gladly acquired through relevant internships

Knowledge of signal processing, hypothesis testing, Bayesian modelling, lifetime analysis
Sound knowledge of Python and relevant libraries (e. g. pandas, numpy, tf , statsmodel, scipy, scikit-learn)

Understanding of basic programming principles and modern DevOps Tooling are required e. g. SQL, Git, Docker, Jenkins
First experience in the creation, deployment and operation of AI services, ideally in an API-first approach
Experience with running machine learning experiments on the cloud and using AzureML services for results and artefacts traceability would be preferred, also usage of Azure Synapse analytics is plus
Past experience with big data analyses will be an advantage, ideally in the context of condition monitoring
Knowledge of NLP methods is a plus as well as of model maintenance (data drift/ model drift) in production would be helpful
Very good knowledge of spoken and written English, German is a plus
Open to new cloud technologies and data science methods and continuously striving to improve
Distinct analytical competence, ability to concentrate and communication skills paired with an agile and product-oriented mindset

As a global company with employees around the world, it is important to us that we treat each other with respect and value all ideas and perspectives. By appreciating our differences, we inspire creativity and drive innovation. In this way, we contribute to sustainable value creation for our stakeholders and society as a whole. Together, we advance how the world moves.
Exciting assignments and outstanding development opportunities await you because we impact the future with innovation. We look forward to your online application.
www.schaeffler.com/careers
Your contact
Schaeffler Technologies AG & Co. KG
Kornelija Jakir
+4915151508865
Keywords: Experienced; Engineer;
Full-Time ; Unlimited

; Information Technology & Digitalization;

Job Segment: Cloud, Test Engineer, Testing, User Experience, Software Engineer, Technology, Engineering

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